SoroushVahidi/scidocs huggingface dataset:
Cochrane handbook for systematic reviews of interventions
(2008)

Chapter 1: Introduction .......................................................................... 3 Key points .................................................................................. 3 1.1 Cochrane ................................................................................ 3 1.1.1 What is Cochrane? ......................................................................... 4 1.1.2 A brief history of Cochrane .................................................................... 4 1.1.3 Cochrane organization and structure .............................................................. 5 1.2 Systematic reviews .......................................................................... 5 1.2.1 The need for systematic reviews ................................................................. 5 1.2.2 What is a systematic review? ................................................................... 5 1.3 Cochrane Reviews .......................................................................... 6 1.3.1 Reviews of the effects of interventions ............................................................. 6 1.3.2 Reviews of diagnostic test accuracy .............................................................. 6 1.3.3 Overviews of Reviews ....................................................................... 6 1.3.4 Reviews of methodology ...................................................................... 7 1.4 Publication of Cochrane Reviews ................................................................. 7 1.4.1 The Cochrane Library ....................................................................... 7 1.5 Handbook structure .......................................................................... 8 1.6 Chapter information .......................................................................... 8 1.7 References ............................................................................... 8 Chapter 3: Reporting the review ..................................................................... 9 3.
Simplified markov random fields for efficient semantic labeling of 3D point clouds
(2012)

In this paper, we focus on 3D point cloud classification by assigning semantic labels to each point in the scene. We propose to use simplified Markov networks to model the contextual relations between points, where the node potentials are calculated from point-wise classification results using off-the-shelf classifiers, such as Random Forest and Support Vector Machines, and the edge potentials are set by physical distance between points. Our experimental results show that this approach yields comparable if not better results with improved speed compared with state-of-the-art methods. We also propose a novel robust neighborhood filtering method to exclude outliers in the neighborhood of points, in order to reduce noise in local geometric statistics when extracting features and also to reduce number of false edges when constructing Markov networks. We show that applying robust neighborhood filtering improves the results when classifying point clouds with more object categories.
Contactless vital signs measurement for self-service healthcare kiosk in intelligent building
(2018)

Contactless healthcare system is becoming a new trend to solve the existing healthcare system limitations. High flexibility and accessibility are the main advantages of contactless measurements. In this paper, we proposed a contactless healthcare kiosk for intelligent building application. The Remote Imaging Photoplethysmography (rIPPG) based framework to measure multiple human's vital signs, including pulse rate (PR), respiratory rate (RR), and systolic blood pressure (SBP) was developed to fulfill the performance requirement with a personal computer. Our experimental results show the Mean Absolute Error (MAE) of each measured parameters are 1.7 BPM, 0.41 BPM and 8.15 mmHg for PR, RR, and SBP respectively compared with clinical apparatus. These statistical numbers show the feasibility of using the contactless based system to measure numerous vital signs and also meet the requirements of standard healthcare device.
Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
(2016)

Massive Online Open Courses (MOOCs) which were introduced in 2008 has since drawn attention around the world for both its advantages as well as criticism on its drawbacks. One of the issues in MOOCs which is the lack of interactivity with the instructor has brought conversational bot into the picture to fill in this gap. In this study, a prototype of MOOCs conversational bot, MOOC-bot is being developed and integrated into MOOCs website to respond to the learner’s enquiries using text or speech input. MOOC-bot is using the popular Artificial Intelligence Markup Language (AIML) to develop its knowledge base, leverage from AIML’s capability to deliver appropriate responses and can be quickly adapted to new knowledge domains. The system architecture of MOOC-bot consists of knowledge base along with AIML interpreter, chat interface, MOOCs website and Web Speech API to provide speech recognition and speech synthesis capability. The initial MOOC-bot prototype has the general knowledge from the past Loebner Prize winner ALICE, course’s frequent asked questions, and a course’s content offered by Universiti Teknikal Malaysia Melaka (UTeM). The evaluation of MOOC-bot based on the past competition questions from Chatterbox Challenge (CBC) and Loebner Prize has shown that it was able to provide correct answers most of the time during the test and demonstrated the capability to prolong the conversation. The advantages of MOOCbot such as able to provide 24-hour service that can serve different time zones, able to have knowledge in multiple domains, and can be shared by multiple sites simultaneously have outweighed its existing limitations.
Executive Involvement and Participation in the Management of Information Technology
(1990)

Executive support is often prescribed as critical for fully tapping the benefits of information technology (IT). However, few investigations have attempted to determine what type of executive support is likely or organizationally appropriate. This article puts forward alternative models of executive support. The models are tested by examining chief executive officers’ behaviors in and perceptions of IT activities. CEOs and information systems executives are surveyed and further data collected from industry handbooks and from chairmen’s annual letters to shareholders. The results suggest that executive involvement (a psychological state) is more strongly associated with the firm’s progressive use of IT than executive participation (actual behaviors) in IT activities. Executive involvement is influenced by a CEO’s participation, prevailing organizational conditions, and the executive’s functional background. CEO’s perceptions about the importance of IT in their firms were generally positive, although they participated in IT activities rather infrequently.
Multiscale Markov Decision Problems: Compression, Solution, and Transfer Learning
(2012)

Many problems in sequential decision making and stochastic control often have natural multiscale structure: sub-tasks are assembled together to accomplish complex goals. Systematically inferring and leveraging hierarchical structure, particularly beyond a single level of abstraction, has remained a longstanding challenge. We describe a fast multiscale procedure for repeatedly compressing, or homogenizing, Markov decision processes (MDPs), wherein a hierarchy of sub-problems at different scales is automatically determined. Coarsened MDPs are themselves independent, deterministic MDPs, and may be solved using existing algorithms. The multiscale representation delivered by this procedure decouples sub-tasks from each other and can lead to substantial improvements in convergence rates both locally within sub-problems and globally across sub-problems, yielding significant computational savings. A second fundamental aspect of this work is that these multiscale decompositions yield new transfer opportunities across different problems, where solutions of sub-tasks at different levels of the hierarchy may be amenable to transfer to new problems. Localized transfer of policies and potential operators at arbitrary scales is emphasized. Finally, we demonstrate compression and transfer in a collection of illustrative domains, including examples involving discrete and continuous statespaces.
Three-phase power factor correction rectifier applied to wind energy conversion systems
(2008)

This paper proposes a topology of three-phase controlled rectifier feasible for small wind energy conversion systems. This rectifier uses six wires of the generator and allows for the operation with high power factor, increasing the generator efficiency. One Cycle Control (OCC) was used in the rectifier control. This study is concerned with the operating principle, a design example, and some simulation and experimental results for a 5 kW prototype.
Imbalanced Deep Learning by Minority Class Incremental Rectification
(2018)

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.
Learning a dense multi-view representation for detection, viewpoint classification and synthesis of object categories
(2009)

Recognizing object classes and their 3D viewpoints is an important problem in computer vision. Based on a part-based probabilistic representation [31], we propose a new 3D object class model that is capable of recognizing unseen views by pose estimation and synthesis. We achieve this by using a dense, multiview representation of the viewing sphere parameterized by a triangular mesh of viewpoints. Each triangle of viewpoints can be morphed to synthesize new viewpoints. By incorporating 3D geometrical constraints, our model establishes explicit correspondences among object parts across viewpoints. We propose an incremental learning algorithm to train the generative model. A cellphone video clip of an object is first used to initialize model learning. Then the model is updated by a set of unsorted training images without viewpoint labels. We demonstrate the robustness of our model on object detection, viewpoint classification and synthesis tasks. Our model performs superiorly to and on par with state-of-the-art algorithms on the Savarese et al. 2007 and PASCAL datasets in object detection. It outperforms all previous work in viewpoint classification and offers promising results in viewpoint synthesis.
Data-swapping --a Technique for Disclosure Control
(2002)

In recent years there has been increasing concern about the confidentiality of computerized databases. This has led to a fast growing interest in the development of techniques for controlling the disclosure of information from such databases about individuals, both natural and legal persons. 3 Much of this interest has been focused on the release of statistical tabulations and microdata files. In this paper, we introduce a new technique, data-swapping, that addresses these interests. This technique can be used to both produce microdata and release statistical tabulations so that confidentiality is not violated. The ideas presented in this report are tentative and have not been tested on real-life data. The illustrations provided in the text are based on small-scale numerical experiments, and serve to illustrate the use of data-swapping for disclosure control. Before the proposed techniques can be used on a large scale, more research will be needed. Directions for this research are pointed out throughout the report. This report is organized in two parts. In Part I, we present the underlying idea of the technique. In Part II, we present some theory and methods.
Sequence to sequence learning for unconstrained scene text recognition
(2016)

In this work we present a state-of-the-art approach for unconstrained natural scene text recognition. We propose a cascade approach that incorporates a convolutional neural network (CNN) architecture followed by a long short term memory model (LSTM). The CNN learns visual features for the characters and uses them with a softmax layer to detect sequence of characters. While the CNN gives very good recognition results, it does not model relation between characters, hence gives rise to false positive and false negative cases (confusing characters due to visual similarities like"g"and"9", or confusing background patches with characters; either removing existing characters or adding non-existing ones) To alleviate these problems we leverage recent developments in LSTM architectures to encode contextual information. We show that the LSTM can dramatically reduce such errors and achieve state-of-the-art accuracy in the task of unconstrained natural scene text recognition. Moreover we manually remove all occurrences of the words that exist in the test set from our training set to test whether our approach will generalize to unseen data. We use the ICDAR 13 test set for evaluation and compare the results with the state of the art approaches [11, 18]. We finally present an application of the work in the domain of for traffic monitoring.
Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions
(2012)

BACKGROUND
Although web-based interventions for promoting health and health-related behavior can be effective, poor adherence is a common issue that needs to be addressed. Technology as a means to communicate the content in web-based interventions has been neglected in research. Indeed, technology is often seen as a black-box, a mere tool that has no effect or value and serves only as a vehicle to deliver intervention content. In this paper we examine technology from a holistic perspective. We see it as a vital and inseparable aspect of web-based interventions to help explain and understand adherence.


OBJECTIVE
This study aims to review the literature on web-based health interventions to investigate whether intervention characteristics and persuasive design affect adherence to a web-based intervention.


METHODS
We conducted a systematic review of studies into web-based health interventions. Per intervention, intervention characteristics, persuasive technology elements and adherence were coded. We performed a multiple regression analysis to investigate whether these variables could predict adherence.


RESULTS
We included 101 articles on 83 interventions. The typical web-based intervention is meant to be used once a week, is modular in set-up, is updated once a week, lasts for 10 weeks, includes interaction with the system and a counselor and peers on the web, includes some persuasive technology elements, and about 50% of the participants adhere to the intervention. Regarding persuasive technology, we see that primary task support elements are most commonly employed (mean 2.9 out of a possible 7.0). Dialogue support and social support are less commonly employed (mean 1.5 and 1.2 out of a possible 7.0, respectively). When comparing the interventions of the different health care areas, we find significant differences in intended usage (p=.004), setup (p<.001), updates (p<.001), frequency of interaction with a counselor (p<.001), the system (p=.003) and peers (p=.017), duration (F=6.068, p=.004), adherence (F=4.833, p=.010) and the number of primary task support elements (F=5.631, p=.005). Our final regression model explained 55% of the variance in adherence. In this model, a RCT study as opposed to an observational study, increased interaction with a counselor, more frequent intended usage, more frequent updates and more extensive employment of dialogue support significantly predicted better adherence.


CONCLUSIONS
Using intervention characteristics and persuasive technology elements, a substantial amount of variance in adherence can be explained. Although there are differences between health care areas on intervention characteristics, health care area per se does not predict adherence. Rather, the differences in technology and interaction predict adherence. The results of this study can be used to make an informed decision about how to design a web-based intervention to which patients are more likely to adhere.
Age-related changes in grey and white matter structure throughout adulthood
(2010)

Normal ageing is associated with gradual brain atrophy. Determining spatial and temporal patterns of change can help shed light on underlying mechanisms. Neuroimaging provides various measures of brain structure that can be used to assess such age-related change but studies to date have typically considered single imaging measures. Although there is consensus on the notion that brain structure deteriorates with age, evidence on the precise time course and spatial distribution of changes is mixed. We assessed grey matter (GM) and white matter (WM) structure in a group of 66 adults aged between 23 and 81. Multimodal imaging measures included voxel-based morphometry (VBM)-style analysis of GM and WM volume and diffusion tensor imaging (DTI) metrics of WM microstructure. We found widespread reductions in GM volume from middle age onwards but earlier reductions in GM were detected in frontal cortex. Widespread age-related deterioration in WM microstructure was detected from young adulthood onwards. WM decline was detected earlier and more sensitively using DTI-based measures of microstructure than using markers of WM volume derived from conventional T1-weighted imaging.
The effects of a 6-week plyometric training program on agility.
(2006)

The purpose of the study was to determine if six weeks of plyometric training can improve an athlete's agility. Subjects were divided into two groups, a plyometric training and a control group. The plyometric training group performed in a six week plyometric training program and the control group did not perform any plyometric training techniques. All subjects participated in two agility tests: T-test and Illinois Agility Test, and a force plate test for ground reaction times both pre and post testing. Univariate ANCOVAs were conducted to analyze the change scores (post - pre) in the independent variables by group (training or control) with pre scores as covariates. The Univariate ANCOVA revealed a significant group effect F2,26 = 25.42, p=0.0000 for the T-test agility measure. For the Illinois Agility test, a significant group effect F2,26 = 27.24, p = 0.000 was also found. The plyometric training group had quicker posttest times compared to the control group for the agility tests. A significant group effect F2,26 = 7.81, p = 0.002 was found for the Force Plate test. The plyometric training group reduced time on the ground on the posttest compared to the control group. The results of this study show that plyometric training can be an effective training technique to improve an athlete's agility. Key PointsPlyometric training can enhance agility of athletes.6 weeks of plyometric training is sufficient to see agility results.Ground reaction times are decreased with plyometric training.
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
(2018)

Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alternative to spatial convolutions. We fuse shifts and point-wise convolutions to construct end-to-end trainable shift-based modules, with a hyperparameter characterizing the tradeoff between accuracy and efficiency. To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters; we additionally demonstrate the operation's resilience to parameter reduction on ImageNet, outperforming ResNet family members. We finally show the shift operation's applicability across domains, achieving strong performance with fewer parameters on image classification, face verification and style transfer.
Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video
(2017)

Omnidirectional video records a scene in all directions around one central position. It allows users to select viewing content freely in all directions. Assuming that viewing directions are uniformly distributed, the isotropic observation space can be regarded as a sphere. Omnidirectional video is commonly represented by different projection formats with one or multiple planes. To measure objective quality of omnidirectional video in observation space more accurately, a weighted-to-spherically-uniform quality evaluation method is proposed in this letter. The error of each pixel on projection planes is multiplied by a weight to ensure the equivalent spherical area in observation space, in which pixels with equal mapped spherical area have the same influence on distortion measurement. Our method makes the quality evaluation results more accurate and reliable since it avoids error propagation caused by the conversion from resampling representation space to observation space. As an example of such quality evaluation method, weighted-to-spherically-uniform peak signal-to-noise ratio is described and its performance is experimentally analyzed.
Comparative Study and Enhancement of Camera Tampering Detection Algorithms
(2016)

Recently the use of video surveillance systems is widely increasing. Different places are equipped by camera surveillances such as hospitals, schools, airports, museums and military places in order to ensure the safety and security of the persons and their property. Therefore it becomes significant to guarantee the proper working of these systems. Intelligent video surveillance systems equipped by sophisticated digital camera can analyze video information's and automatically detect doubtful actions. The camera tampering detection algorithms may indicate that accidental or suspicious activities have occurred and that causes the abnormality works of the video surveillance. It uses several techniques based on image processing and computer vision. In this paper, comparative study of performance of four algorithms that can detect abnormal disturbance for video surveillance is presented also a new proposed method is exposed.